Papers

CONFERENCE (INTERNATIONAL)

Predicting Causes of Reformulation in Intelligent Assistants

Intelligent assistants (IAs) such as Siri and Cortana
conversationally interact with users and execute a wide range of actions
(e.g., searching the Web, setting alarms, and chatting). IAs can support
these actions through the combination of various components such as
automatic speech
recognition, natural language understanding, and language generation.
However, the complexity of these components hinders developers from
determining which component causes an error. To remove this hindrance, we
focus on reformulation, which is a useful signal of user dissatisfaction,
and propose a method to predict the reformulation causes. We evaluate the
method using the user logs of a commercial IA. The experimental results have
demonstrated that features designed to detect the error of a specific
component improve the performance of reformulation
cause detection.